OpinionRank: Extracting Ground Truth Labels from Unreliable Expert Opinions with Graph-Based Spectral Ranking
This addresses the challenge of unreliable labeling in crowdsourcing for machine learning, offering a scalable and efficient solution, though it appears incremental as it builds on existing graph-based methods.
The paper tackles the problem of obtaining reliable labels from unreliable crowdsourced annotations by proposing OpinionRank, a graph-based spectral algorithm that integrates these annotations into trustworthy labels for supervised or semi-supervised learning. The experiments show that OpinionRank performs favorably compared to more parameterized algorithms, is scalable to large datasets and many label sources, and requires fewer computational resources.
As larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information with which to train sophisticated models. To address this problem, crowdsourcing has emerged as a popular, inexpensive, and efficient data mining solution for performing distributed label collection. However, crowdsourced annotations are inherently untrustworthy, as the labels are provided by anonymous volunteers who may have varying, unreliable expertise. Worse yet, some participants on commonly used platforms such as Amazon Mechanical Turk may be adversarial, and provide intentionally incorrect label information without the end user's knowledge. We discuss three conventional models of the label generation process, describing their parameterizations and the model-based approaches used to solve them. We then propose OpinionRank, a model-free, interpretable, graph-based spectral algorithm for integrating crowdsourced annotations into reliable labels for performing supervised or semi-supervised learning. Our experiments show that OpinionRank performs favorably when compared against more highly parameterized algorithms. We also show that OpinionRank is scalable to very large datasets and numbers of label sources, and requires considerably fewer computational resources than previous approaches.